Gallery
Here you can view some of the images I created in R.
Figure 1. Prediction of enemy release as an important driver of species invasions, where invasiveness is predicted by a significant interaction between enemy release and top-down control. Each point represents an introduced species. The yellow line indicates the effect of enemy release given the maximum amount of top-down control observed, the black line indicates the effect of enemy release given no top-down control. Invasiveness and enemy release are treated as continuous variables with P representing population density, and E representing enemy abundance. Subscripts denote whether the trait was measured in the invasive (I) or native (N) range. Superscripts in the “top-down control” legend indicate density with enemies (+) and without enemies (–).
Figure 2. Path analysis of Alliaria petiolata rosette size. Rosette size was mean standardized while other variables were z-standardized to give SD-standardized performance gradients. Arrows denote the direction of the effect; green arrows indicate a positive effect and red arrows indicate a negative effect. Arrow thickness denotes the magnitude of the effect and darker arrows indicate greater significance. General investment (PC1) denotes the production of both glucosinolates and chlorophyll A, while greater relative investment (PC2) values indicate more glucosinolate and less chlorophyll A production. TLA denotes the total leaf area of Acer saccharum at the end of year 1. The solid arrows connecting PC1 and PC2 to A. petiolata survival reflect direct selection on PC1 and PC2 through a direct mechanism, while the dashed arrows reflect direct selection on PC1 and PC2 through the indirect mechanism of affecting Acer saccharum TLA.
Figure 3. Example of machine learning performed in Python. The program identified the plant in the input images (top) and yielded the output images (bottom) to demonstrate which parts of the image have been identified as plant. I could then extract and store data pertaining to the plant. This program was run to obtain data from > 5000 images.
Figure 4. Mean growth rate of Garlic Mustard (A.petiolata), colored by genotype and distributed across competition treatments. Using data extracted from images of A.petiolata over a summer (example above), I was able to analyze growth rate in R.
Figure 5. Proportion of women reporting cannabis consumption at some point in pregnancy (a), current consumption during pregnancy (b) and intent to consume cannabis while breastfeeding (c). Proportions are distributed across three levels of educational attainment, with and without partners who consume cannabis. For each group of women, coloured bars represent the proportion of women who reported the outcome and lines represent the 95% confidence intervals around each proportion.
Figure 6. Fitness reaction norms for the relationship between enemy abundance (E) and fitness (W) for a susceptible (light orange) and a resistant (blue) genotype. The three columns show different scenarios in which the defence cost—benefit ratios are high (a,d,g), moderate (b,e,h) or low (c,f,i). The three rows show different scenarios for variation in the abundance of enemies in the native (N, green) and introduced ranges (I, yellow). Of these nine scenarios, only one supports the evolution of increased competitive ability (EICA) in which natural selection for defence reverses between ranges (panel e).
Figure 7. Effect of competition on A.petiolata and A.saccharum under different competition regimes.
